A Comparative Lipidomics Platform for Chemotaxonomic …€¦ · and ionization protocols. Chromatographic Design We harvested mycobacteria from plates, broth, or infected mice and

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A comparative lipidomics platform for chemotaxonomic analysis of mycobacteriumtuberculosisLayre, Emilie; Sweet, Lindsay; Hong, Sunhee; Madigan, Cressida A.; Desjardins, Danielle;Young, David C.; Cheng, Tan-Yun; Armand, John W.; Kim, Keunpyo; Shamputa, Isdore C.Published in:Chemistry & Biology

DOI:10.1016/j.chembiol.2011.10.013

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Chemistry & Biology

Article

A Comparative Lipidomics Platformfor Chemotaxonomic Analysisof Mycobacterium tuberculosisEmilie Layre,1 Lindsay Sweet,1 Sunhee Hong,1 Cressida A. Madigan,1 Danielle Desjardins,1 David C. Young,1

Tan-Yun Cheng,1 John W. Annand,1 Keunpyo Kim,2 Isdore C. Shamputa,3 Matthew J. McConnell,1 C. Anthony Debono,1

Samuel M. Behar,1 Adriaan J. Minnaard,4 Megan Murray,5 Clifton E. Barry, III,3 Isamu Matsunaga,6

and D. Branch Moody1,*1Division of Rheumatology, Immunology, and Allergy, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA2Biostatistics, MedImmune, One MedImmune Way, Gaithersburg, MD 20878, USA3Tuberculosis Research Section, Laboratory of Clinical Infectious Diseases, National Institute of Allergy and Infectious Diseases,

National Institutes of Health, Bethesda, MD 20892, USA4Stratingh Institute for Chemistry, University of Groningen, 9747 AG, Groningen, The Netherlands5Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA6Laboratory of Cell Regulation, Department of Viral Oncology, Institute for Virus Research Kyoto University, Kyoto 606-8507, Japan

*Correspondence: [email protected] 10.1016/j.chembiol.2011.10.013

SUMMARY

The lipidic envelope of Mycobacterium tuberculosispromotes virulence in many ways, so we developeda lipidomics platform for a broad survey of cell walls.Here we report two new databases (MycoMass,MycoMap), 30 lipid fine maps, and mass spectrom-etry datasets that comprise a static lipidome. Fur-ther, by rapidly regenerating lipidomic datasetsduring biological processes, comparative lipidomicsprovides statistically valid, organism-wide compari-sons that broadly assess lipid changes during infec-tion or among clinical strains of mycobacteria. Usingstringent data filters, we tracked more than 5,000molecular features in parallel with few or no false-positive molecular discoveries. The low error ratesallowed chemotaxonomic analyses of mycobacteria,which describe the extent of chemical change ineach strain and identified particular strain-specificmolecules for use as biomarkers.

INTRODUCTION

Mycobacterium tuberculosis elaborates one of nature’s most

complex lipid envelopes, which forms a barrier with the human

host. This multilayered cell wall contains an inner phospholipid

bilayer and an outer layer of a-alkyl, b-hydroxy mycolic acids

and other long-chain lipids (Daffe and Draper, 1998; Hoffmann

et al., 2008; Zuber et al., 2008). During a decades-long infection

cycle, this unusually thick and hydrophobic barrier controls

import of essential host metabolites and passage of antituber-

cular drugs (Adams et al., 2011), and it releases lipid antigens

and adjuvants into the host (Geisel et al., 2005). During infection

(Kondo et al., 1970), nutrient deprivation (Rustad et al., 2008;

Singh et al., 2009), and genetic regulation (Raman et al., 2006),

mycobacteria respond by broadly reorganizing their cell walls,

Chemistry & Biology 18, 1537–154

providing a need for systems biology approaches to globally

measure bacterial responses. This goal has been achieved for

transcripts (Homolka et al., 2010; Rohde et al., 2007; Schnap-

pinger et al., 2003) and proteins (Kruh et al., 2010) and has

been partially achieved for the cytosolic metabolites that do

not form membranes (de Carvalho et al., 2010; Marrero et al.,

2010).

For lipids, sensitive mass spectrometry and nuclear magnetic

resonance profiling methods are emerging that nearly simulta-

neously detect many types of lipids. These methods sensitively

detect several previously identified lipid families whose mass/

charge ratios (m/z) (Jain et al., 2007; Matsunaga et al., 2004;

Sartain et al., 2011) or nuclear magnetic resonance (NMR)

signals (Mahrous et al., 2008) match predefined values. An ideal

lipidomics system would offer both broad coverage of many

thousands of molecules in mycobacterial lipidomes as well as

the ability to convert any unnamed compounds of known mass

to named compounds through accurate mass retention time

(AMRT) databases or collisional mass spectrometry. In addition,

bioinformatics methods for organizing and comparing all lipids

among two bacteria or two bacterial states are needed. Myco-

bacterial lipids, especially large uncharged lipids associated

with the mycolate layer, differ from well-studied anionic phos-

pholipids of model organisms with regard to mass and their ioni-

zation properties in mass spectrometry. Therefore, approaching

this goal required development of new mycobacteria-specific

mass spectral databases and software protocols for automated

ion finding, as well as broadly separating chromatography opti-

mized for unusually hydrophobic lipids associated with the my-

colate membrane.

Here we report two new mycobacterial databases (Myco-

Mass, MycoMap), an integrated set of software methods, and

a universal separation method, which, when coupled with

collisional mass spectrometry, meet most of these goals.

Using an extract of lipids from one bacterial strain taken at one

point in time, these methods provide a snapshot profile of

more than 5,000 molecular features, which approach the goal

of solving one static lipidome. Further, comparative lipidomics

9, December 23, 2011 ª2011 Elsevier Ltd All rights reserved 1537

mailto:[email protected]

http://dx.doi.org/10.1016/j.chembiol.2011.10.013

Chemistry & Biology

Mycobacterial Lipidomics

seeks to measure lipid changes in an organism-wide basis as

biological events unfold over time or to broadly characterize

the molecules that differ between any two bacteria (chemotax-

onomy). These kinds of applications require that many lipi-

domes be generated in a short period of time and bio-

finformatic methods for aligning mass spectrometry signals

across multiple datasets to identify the subset of changed

molecules. Taking advantage of stringent data filters that

generated a low false-positive rate, this comparative lipidomics

platform could reliably detect thousands of molecular changes

after infection or among mycobacterial strains, enabling

chemotaxonomic analyses of experimental and clinical strains

of mycobacteria.

RESULTS

The MycoMass DatabaseTo compile the expected m/z from known mycobacterial lipids,

we first created an inventory of literature reports relating to

M. tuberculosis and other medically important mycobacteria.

Lipids were organized according to LIPID MAPS criteria, using

a four-level classification divided into seven categories and

23 classes based on generic structures, from which 43 sub-

classes and 58 families were defined based on differences in

the number or nature of carbohydrate or lipid moieties (Fig-

ure 1) (Fahy et al., 2005). Each lipid family contained an

average of 95 alkylforms, which are individual molecules

differing in the length and saturation of fatty acyl or polyketide

backbones. The resulting MycoMass database catalogs more

than 5,000 neutral species (M) and their deduced negative

and positive ions, for a total of 32,438 entries (Figure S1 avail-

able online).

Like the independent effort of Sartain et al. (2011), MycoMass

represented the first step toward a mycobacterial lipidome,

because it organized and defined the scope of known lipid

families requiring detection. Second, MycoMass also served

as the input data that allow for mass comparison with detected

molecules to support development of new automated ion

annotation protocols. Third, because more than 40 of the 58

cataloged lipid families are lacking in eukaryotic cells or

Gram-negative bacteria, MycoMass provides a quantitative

summary of the divergence of the mycobacterial lipidome

from widely studied model organisms. MycoMass lists mycoke-

tides, phthiocerols, menaquinones, mycolactones, and hy-

droxyphthioceranic, mycolipanolic, phthienoic, mycolipodienic,

mycosanoic, mycocerosic, and mycolic acids, as well as many

other lipids found mainly or exclusively in mycobacteria and

closely related actinobacteria. Therefore, MycoMass contains

specific mass targets that could serve as biomarkers of in-

fected cells. Many neutral lipids are distinct in mass and struc-

ture from anionic phospholipids that dominate in Gram-nega-

tive bacteria or eukaryotic cells. The divergence of MycoMass

entries from databases for model organisms illustrate why the

latter, despite their high quality, do not support studies of

mycobacterial pathogenesis (Dennis et al., 2010; Quehenberger

et al., 2010; van Meer et al., 2007). Also, the unusual hydropho-

bicity and high mass of mycobacterial neutral lipids necessi-

tated the development of new methods of chromatography

and ionization protocols.

1538 Chemistry & Biology 18, 1537–1549, December 23, 2011 ª2011

Chromatographic DesignWe harvested mycobacteria from plates, broth, or infected mice

and extracted lipids with chloroform and methanol. Although

more complex than simultaneous (i.e., shotgun) ionization,

chromatographic separation of lipids in a mixture prior to ioniza-

tion offers advantages. Column retention predicts polarity of

unknown molecules, facilitating their identification. Chromatog-

raphy separates molecules of similar mass in time, creating

a large two-dimensional AMRT area to resolve individual compo-

nents within mixtures containing thousands of ions. Perhaps the

key advantage of chromatography is reducing the chemical

dissimilarity of molecules entering the electrospray source at

any moment. Dissimilar molecules can dramatically alter the

efficiency of electrospray ionization, leading to cross-suppres-

sion, a phenomenon that particularly affects apolar lipids that

dominate mycobacteria (Taylor, 2005).

Our first generation method used acetone precipitation to

separate lipids into batches, followed by several reversed-phase

high-performance liquid chromatography (HPLC) methods to

optimize separation (Figure S2A). Multiple datasets were then

reconciled into one lipidome. As expected, phospholipids and

mycolyl glycolipids precipitated (Borgstrom, 1952; Takayama

and Armstrong, 1976), but triglycerides, phthiocerol dimyco-

cerosates (PDIMs), and many other lipids partitioned into both

phases (Figure S2B). Any biological variable modifying the abun-

dance of individual molecules in the mixture can change the

partition coefficient of other lipids. Therefore, fluid-phase sepa-

rations, despite their wide use for targeted analyses, are unsuit-

able for lipidomics. This development effort highlights a general

difference between lipidomic and typical analytical lipid chem-

istry problems: rather than optimizing for any single lipid, all

new methods must be validated for extremely diverse lipids in

one sample.

To avoid partitioning and errors in reconciling many datasets,

our second generation replaced five HPLC systems with one

single-step method for the analysis of total lipid extracts (Fig-

ure 2A, black). The key challenge was to devise a normal-phase

chromatography that solubilizes, separates, and allows ioniza-

tion of highly diverse molecules using one general method.

We evaluated each method change on the whole dataset of

unnamed molecules (Figure 2B) and four named benchmark

lipids of low, intermediate, and high polarity spanning a wide

range of signal intensity (102–107 counts). These benchmark

lipids were PDIM, trehalose monomycolate, diacylated sulfo-

glycolipids, and cardiolipin, representing apolar lipids, glyco-

lipids, sulfolipids, and phospholipids, respectively (Figure 3A).

By tracking these, we successfully developed a hexanes/

isopropanol/methanol solvent system for normal-phase chroma-

tography that separates families with even density and allowed

sensitive detection over a wide dynamic range (Figure 2B). This

simplified system reduced lipidome generation time from several

days to 45min, allowing generation of 30 lipidomes in 1 day. This

advance fulfilled a crucial performance goal for comparative lip-

idomics, which requires serial generation of lipidomes in tripli-

cate under rigorously comparable conditions.

Optimizing Lipidomic DetectionWhereas metabolomics conventionally focuses on aqeous cyto-

solic intermediates that generate energy (de Carvalho et al.,

Elsevier Ltd All rights reserved

Figure 1. MycoMass Database Content

List of the lipids cataloged in theMycoMass database (Figure S1). This database follows the LIPIDMAPS organizational tree and uses lipid families’ names found

in the mycobacterial literature in level 4. Phosphatidylinositol mannosides (PIMx) contain 1–6 mannosyl residues (x) and sulfoglycolipids (AcxSGL) contain 2–4

fatty acyl chains (x). Alkylforms vary by the saturation and carbon length of acyl chains and/or by the length of carbon backbones.

Chemistry & Biology


2010; Fruh et al., 2010; Lakshmanan et al., 2011), the emerging

specialty of lipidomics provides detailed information about

hydrophobic molecules that form membrane barriers. We in-

vented a lipidomics system to investigate how themycobacterial

cell wall, acting as the interface between the host and pathogen,


regulates transport of drugs, antigens, and metabolites from the

phagosome into the bacterium. Cytosol profiling typically uses

ethyl acetate to extract aqueous-soluble compounds, empha-

sizing somewhat polar molecules of lower mass (50–300 atomic

mass units) and rapid turnover (seconds). Lipidomic methods


Figure 2. Lipidomics Platform

(A) Lipid extracts (dark blue) enter a workflow involving a universal normal-phase HPLC-MS system (black), software-assisted raw data extraction (green),

computational comparative analysis (red), database and dataset annotation (purple), and molecular discovery through collisional mass spectrometry (light blue).

This second-generation system for comparative profiling emphasizes a single-step chromatography system, in contrast to a first-generation method that uses

fluid phase separation and multiple HPLC systems (Figure S2).

(B) Extracted ion chromatograms of the overall features detected with high, intermediate, and low intensity by analyzing M. tuberculosis H37Rv total lipids.

Chemistry & Biology


extract higher-mass (300–3,000 atomic mass units), low-turn-

over (days) compounds into chloroform-containing solvents,

which, for mycobacteria, are unusually diverse long-chain lipids.

Comparative lipidomics requires precise normalization of

input lipids as well as reproducible and sensitive detection of

aligned, replicate lipidomes. After initially adopting published

ionization conditions for anionic membrane phospholipids

(Kruve et al., 2010), reduction of countercurrent gas flow and

increasing voltage produced greater than 100-fold increases in

sensitivity and revealed high mass neutral lipids that were other-

wise undetectable, such as trehalosemonomycolate (Figures 3B

and 3C). These gains resulted from generating greater force

toward the detector and were seen for many ions in the myco-

bacterial lipidome. This dramatic and broad-based improvement

in sensitivity resulted from adjustments of ionization conditions

that are better suited to the larger, uncharged lipids that populate

the mycolate layer.

Interlipidome comparisons rely on precise normalization of

input lipids, which was accomplished using cultures harvested

at a similar optical density of 0.6 (±0.1) and weights determined


with less than 1% SD. Using this method, the SD of triplicate

intensity measurements of benchmarks was about 2% for tech-

nical replicates and 10% for biological replicates (Figure 3D).

Thus, experimental error was low in absolute terms and derived

mainly from non-uniform bacterial culture rather than LC-MS

detection. Normalization at the detection level was further

confirmed by continuous detection of calibrants, tracking total

ion current generated by all lipids, and by monitoring abundant

structural lipids that serve as housekeeping controls. For ex-

ample, cardiolipin showed highly reproducible intensity among

biological replicates (Figure 3D) and did not change significantly

between diverse samples subject to biological variables (data

not shown). Serial dilution of input lipids determined that

500 mg/ml provided nonsaturating and near linear detection of

benchmarks (Figure 3E) and other lipids over a 10-fold or higher

change in concentration. Thus, a 20 ml injection from scant in vivo

specimens yet produced allowed sensitive and broad lipidomic

coverage with benchmark lipids detected below the picogram

range (Figure S3). Therefore, the platform met the criteria for

sensitivity, reproducibility, and relative quantitation.


Figure 3. Validation of a Universal Normal-Phase HPLC-MS Detection

(A–C) Structures (A) of diverse benchmark lipids for HPLC-MS method optimization related to countercurrent gas (B) and source voltage (C) as measured in

biological replicates (D).

(E) Relationship of signal intensity derived from areas under the curves of ion chromatograms to input mass of total lipid. Data are representative of three or more

experiments.

Chemistry & Biology


Mapping the Lipidome of M. tuberculosis

This single-step universal chromatography method was imple-

mented for assigning the retention time (RT) of each lipid family

as an organizing principle for mapping of the lipidome. Large

raw datasets were processed by XCMS for noise filtering, peak

picking, and deconvolution to resolve coeluting ions and peak

alignment across replicates so that features with equivalent

AMRT values are aligned across biological conditions and their


intensities reported in a final data matrix (Smith et al., 2006) (Fig-

ure 2A, green). A feature was defined as a three-dimensional

value ofm/z, RT, and intensity detected in triplicate. Typical anal-

yses of M. tuberculosis lipid extract yielded a data matrix of

�6,000 and �5,000 features acquired in the positive and nega-

tive ionmode, respectively (Figure S4). Evenwhen lacking chem-

ical names, features with high fold change have value asmarkers

of the bacterial response (Figure 2A). Nevertheless, we sought to


Figure 4. Mapping the Lipidome of M. tuberculosis H37Rv

(A) HPLC-MS dataset ofM. tuberculosisH37Rv of�6,000 features, which are three-dimensional coordinates of linkedm/z, RT, and intensity. One lead compound

in each cluster was tentatively identified by automated annotation using MycoMass, confirmed by four analytical criteria, and mapped to the chromatographic

system in positive (B) and negative (C) ion mode.

(B and C) Extracted ion chromatograms of mapped lipid families. Neutral formulas of the studied alkylforms and detected m/z of the respective [M+NH4]+ or

[M+H]+ adducts (B) and [M–H]� forms (C) are indicated. RTs of lipids typically varied by less than 5 s in one experiment, but varied up to 60 s among users with

differing columns. Phosphatidylinositol mannosides (PIMx) are listed according to the number (x) of mannosyl residues. Thirty lipid families mapped in this way

comprise the MycoMap. Features annotation and collisional MS are shown in Figure S4.

Chemistry & Biology


‘‘presolve’’ many key features of M. tuberculosis H37Rv as

named compounds to allow broad monitoring of known

molecules.

We created fine maps of 30 families of lipids by repeatedly

applying a four-step process (Figure 4A). First, when plotted as

RT versus m/z, features self-organize into clusters comprising

many alkylforms with the same head group. This clustering

results from normal-phase chromatography, which resolves all

molecules into separate families, but not alkylforms within

a family. Tight clustering facilitates identification of families and

limits their overlap in RT, reducing molecular heterogeneity and

cross-suppression between disparate lipid families. Second,

clustered features were tentatively assigned to a known lipid

using an in-house designed script using R software (see Supple-


mental Information), allowing automatic naming of features

whose m/z matched within 10 ppm to any entry in MycoMass.

This software achieved tentative annotation of 624 and 366

features in positive and negative ionmode datasets, respectively

(Figure S4).

Step 3 tests the assignments by comparing alkylform diver-

sity, RT, and collisionalMS of feature groups to knownmolecules

(Figure S4). For example, features initially annotated as PDIM by

software were found to match the RT of a synthetic PDIM stan-

dard (3.6 min, data not shown) and appeared as an alkane series

of the expected length (C87-C102) of M. tuberculosis PDIMs

(Figure S4; Figure 5A). One ion of m/z 1371.413 matching the

expected m/z for the ammoniated adduct of C91H180O5 PDIM

(m/z 1371.412) showed the fragments expected of phthiocerol


Figure 5. In Vitro and In Vivo Fine Mapping of M. tuberculosis H37Rv Lipid Families

(A–C) Extracts of M. tuberculosis H37 Rv grown in vitro were subject to detection of PDIM, trehalose monomycolate (A and B), and 28 other lipids (Figure S5)

illustrating the PDIM detected alkylforms (A) and RT profiles (B) that match by color and were confirmed by collisional mass spectrometry (C).

(D)M. tuberculosis Erdman 2.5 grown in brothmedia or in one infectedmousewere similarly analyzed for the PDIMA/A0 andB alkylformswith the indicated overall

carbon number. Similar results were found in separate analysis of three mice (Figure S6).

Chemistry & Biology


and mycocerosyl substructures (Figure 5C). We repeated this

process for 30 annotated lipid families (Figures 4B and 4C;

Figure S4). Because most of the mycobacterial lipidome’s diver-

sity is found in neutral lipids (Figure S1), we obtained larger

datasets in the positive ion mode than in the negative ion

mode (Figures 4B and 4C). This result contrasts with conven-

tional lipidomic studies that emphasize detection of negatively

charged membrane phospholipids of mammalian cells. Both

modes provided a high dynamic range that spanned four

log orders of magnitude, so diacylglycerides, monoacyl PIM,

and other strongly detected lipids (�106 counts), as well as

PDIM, triglycerides, menaquinone, sulfoglycolipids, mannosyl

phosphomycoketide, lysophospholipids, and lower-intensity

PIM families (�104 counts), could be tracked in parallel with

one injection.


Fine MappingFinemapping is a processwhereby each alkylformwithin a family

is separately assigned. For example, PDIM naturally occurs with

between 87 and 102 total carbon atoms within A and B families,

distinguished by methoxy or keto substitutions (Constant et al.,

2002) (Figure 5A). Sixteen length variants in two families predict

32 alkylforms. We detected and mapped all 32 alkylforms as

nearly overlapping chromatograms (Figures 5A and 5B). Simi-

larly, we mapped 28 trehalose mycolates (Figure 5B) and the

alkylforms of 28 other lipid families, covering 318 compounds

to create MycoMap (Figure S5). Fine mapping supports applica-

tions that take advantage of species- or strain-specific patterns

in actinobacteria for clinical diagnosis or chemotaxonomic as-

signment (Song et al., 2009). Furthermore, alkylforms within

a family can change differentially in response to a biological


Chemistry & Biology


variable, so finemapping can describe chemical remodeling. For

example, M. tuberculosis harvested from mouse lung produces

longer PDIM alkylforms compared to bacteria from in vitro

culture, resulting from increased methylmalonate availability in

tissues (Jain et al., 2007). We detected 32 alkylforms of PDIM

produced in infected mouse lungs, which confirmed previously

reported in vivo lengthening (Figure 5D; Figure S6). Thus, fine

mapping illustrates that the broadly comparative method

described here can meet requirements previously accomplished

with targeted ion finding. Detection is adequately sensitive for

lipids extracted from infected tissues, where mammalian lipids

greatly predominate.

Comparative LipidomicsComparative lipidomics requires efficient algorithms to process

raw LC-MS data, measure the intensity of individual chromato-

grams, and align thousands of features across many lipidomes

to generate a data matrix. Our first-generation (Figure S2) and

other (Sartain et al., 2011) data extraction methods pool isotopes

and adducts of deduced neutral molecules (M) as one intensity

value. This approach causes quantitative errors related to

adduct assignment and pooling of many intensity values, which

leads to quantitative errors when count values detected in the

nonlinear range of counts to mass are summed. When

comparing two large datasets, these kinds of errors caused

many ions to be incorrectly assessed as changed molecules,

leading to unacceptably high false-positive molecular finding

rates. Manual inspection of chromatograms improved reliability

but were too cumbersome to evaluate more than 100,000 peaks

in one experiment. Therefore, we implemented XCMS ion finding

algorithm, which treats all ions as separate features and

bypasses ion batching errors. Implementation of reliable auto-

mated ion finding algorithms was the key advance allowing

comparison of datasets with more than 100,000 features.

Next, Mass Profiler Professional software was implemented

for comparisons and statistical analysis of XCMS-generated

data matrices to report changed features and their p values cor-

rected with the Benjamini-Hochberg multiple comparisons test

(Figure 2A, red). To quickly highlight significant changes, results

were displayed in two-dimensional scatter plots of fold change

versus corrected p value, also known as volcano plots (Figures

6A–6D). Because a typical comparative lipidomics experiment

generates hundreds of changed molecules, which collectively

exceed any capacity for detailed biological validation, the over-

riding design objective is limiting false-positive molecular dis-

covery. Therefore, we used stringent filters to remove ions

absent in any replicate and those with intensity values showing

high variance (corrected p > 0.05). We considered a feature to

be changed when its intensity value changed at least two-fold

(Figures 6C and 6D, red dots), which exceeds the sum of the

typical variation observed among biological triplicates (Fig-

ure 3D). Despite these stringent filters, the biofinformatic pipeline

permitted broad coverage, typically 4,000 to 10,000 compari-

sons per experiment. By comparing two triplicate analyses of

the same bacterial culture, the percentage of features that are

described as changed represented the false-positive molecular

discovery rate (Figure 6A). Remarkably, 6,498 pairwise compar-

isons yielded no false-positive results from cumulative errors in

extraction, separation, detection, and software analysis. Similar


analysis for biological replicate cultures showed an error rate of

0.7% (Figure 6B). Thus, errors derive mainly from culture rather

than LC-MS detection. The near zero rate of false molecular

detection provided a blank canvas against which any molecules

changing after introducing biological variables would likely be

caused by the biological variable, setting the stage for chemo-

taxonomy analyses of mycobacteria.

ChemotaxonomyThe goals of chemotaxonomy are two-fold: (1) measure the

number of changed molecules as a descriptor of chemical

relatedness of two bacteria and (2) provide a list of changed

molecules to discover biomarkers. We compared virulent

M. tuberculosis H37Rv with avirulent M. smegmatis, and with

a reference strain of the W Beijing clade of M. tuberculosis

(HN878) (Figures 6C and 6D). Molecular features showing inten-

sity changes that met variance criteria for genuine differences

showed two patterns. For any feature that shows background

signal in one dataset, its intensity is assigned as 1 rather

than 0. Therefore, all or nothing changes in molecules, which

represent the best biomarkers, appear as high (>210) but not

infinite change values. Features with a 2- to 210-fold change rep-

resented features present in both bacteria with altered concen-

trations. These features might represent regulated lipids that

define the physiological state of the bacterium. The intraspecies

and intragenus comparisons detected 648 changes (11%) and

4,339 changes (47%), respectively (Figures 6C and 6D). Thus,

the scope of chemical change correlates with genetic related-

ness, validating the discriminatory potential of this lipidomics

method.

Because each feature contains embedded AMRT information

and can be subjected to automated annotation, this com-

parison can rapidly identify strain specific biomarkers without

further experimentation. For example, the W Beijing lineage of

M. tuberculosis is hypervirulent in mice (Dormans et al., 2004;

Manca et al., 2005; Reed et al., 2004) and has emerged world-

wide as a human pathogen with distinct transmission features

(Glynn et al., 2002). Among 5,886 pairwise comparisons between

M. tuberculosisH37Rv and the clinical reference strain for Beijing

(HN878), we identified 303 features upregulated in the Beijing

strain, of which 69 represented all or nothing changes (Figure 6C,

enlarged). Automated and manual annotation showed that 38

are alternative adducts and isotopes of the same alkane series

with nominal neutral mass values (M) between 1785 and 1911

(Figure 6C, green; Figure S7A). These ions and their key frag-

ments (Figure 6E) correspond to the expectedmassof triglycosy-

lated phenolic glycolipids (PGLs). This virulence-associated

glycolipid has been previously identified on a genetic basis by

intact polyketide synthase 15/1 in the W Beijing lineage, but not

H37Rv, which has a frameshift in this locus (Constant et al.,

2002). Thus, we identified a known strain-specificmolecule using

a rapid and unbiased cell wall screen. Identification of PGLs in

38 distinct molecular forms represents a redundant and

convincing form of detection that is not possible using bio-

informatic methods that batch isotopes and adducts.

Lipidomic versus Targeted Scanning for PGLsThe relationship between Beijing lineage and PGLs was previ-

ously known, but there were no clinical tests for the screening


Figure 6. Comparative Chemotaxonomy

(A–D) Pairwise comparison of extractable lipids represented as volcano plots, showing in red the features meeting criteria for two-fold change and significance

(p < 0.05, corrected for multiple comparison) also indicated as a percentage of all features (n). M. tuberculosis H37Rv lipid extract from one (A) or two (B) liquid

cultures were analyzed in triplicate and compared withM. tuberculosis Beijing HN878 (C) orM. smegmatis (D). Among features uniquely present in the W Beijing

strain (C, inset, and listed Figure S7A), 38 (green) corresponded to isotopes (M, M+1, M+2) and adducts (NH4+ or Na+) of a triglycosylated PGL alkane series, as

illustrated for two alkylforms of nominal masses of 1827 and 1841.

(E) Collisional mass spectrometry of [M+NH4]+ adduct of PGLs confirmed structure composed of a phthiocerol core esterified by C27 and C30mycocerosic acids

(R1COOH, R2COOH).

(F and G) Extracted ion chromatograms of a representative alkylform of the monoglycosylated (m/z 1553.442) or triglycosylated (m/z 1845.554) form of PGLs for

laboratory and patient isolates show sensitive detection that is not confounded by other lipids and separate detection of the two PGL glycoforms. The mass

spectrum of triglycosylated PGLs is shown in Figure S7B.

Chemistry & Biology


of triglycosylated PGLs found in virulent strains, or for comparing

this virulence-associated molecule with the monoglycosylated

form found in Bacillus Calmette-Guerin (BCG) (Daffe and


Laneelle, 1988). Genetic tests of the pks15/1 locus generally

rule out PGL production when abnormal, but are not sufficient

to rule in PGL production, because many other genes are


Chemistry & Biology


needed (Malaga et al., 2008; Perez et al., 2004). Because the

best available chemical test, radio-thin layer chromatography,

requires biosynthetic labeling in biosafety level 3, it is not feasible

in clinical laboratories (Reed et al., 2004). Therefore, despite

considerable interest in the dispersion of Beijing strains world-

wide and direct evidence for PGLs as a virulence factor in

mice, studies of PGLs in human isolates have been limited. Using

AMRT (RT = 4min,m/z 1845.547) and a diagnostic finemap from

the lipidomics platform (Figure 6C), we converted from a broad

scanning mode to a simplified, specific analysis of ions corre-

sponding to monoglycosylated (m/z 1553.442) and triglycosy-

lated (m/z 1845.554) PGLs. Signal intensity is more than

100-fold above baseline levels and is not confounded by any

other ion (Figure 6F; Figure S7B). We applied this test to detect

PGLs in patient isolates from South Korea (Figure 6G) and

identified both mono- and triglycosylated PGLs in isolates geno-

typed as Beijing strains with an intact pks15/1 locus. Further, we

identified an isolate with discordant production of themono- and

triglycosylated PGLs. These studies illustrate the transition from

lipidomic scanning to a focused analysis. In contrast with the

current gold standard test requiring radioactive labeling, this

test uses standard media and is rapid, sensitive, and chemically

specific, because collision-induced dissociation (CID)-MS pro-

vides chemical detail. More generally, all 303 events that meet

statistical and fold change criteria become candidate targets

for strain-specific biomarkers or determinants of W Beijing

physiology.

DISCUSSION

Based on its sensitivity, this comparative lipidomics system can

be used to evaluate any genetic or biological perturbation, even

within infected cells. Based on the low rate of false-positive

molecular discovery, it is possible to embark on unbiased dis-

covery for all molecules regulated by any single gene deletion

or metabolic perturbation. Therefore, this method is currently

being applied to determine cell wall changes induced by antitu-

bercular drugs, evolution of multidrug resistance, dormancy,

cellular infection, and iron deprivation. The development of

a new clinically useful test for the PGL virulence determinant

provides a glimpse of the high value of extending general lipi-

domic maps of model organisms to pathogens with unusual

lipid repertories. A simple test to monitor PGLs in clinical

M. tuberculosis strains allows new investigation to determine

whether the virulence-inducing effects seen in mice might occur

in humans or are outweighed by fitness costs or decreased

transmission from altered immune response (Comas et al.,

2010).

These studies also provide a quantitative estimate of the

scope of current knowledge of the mycobacterial lipidome.

Even using MycoMass, the largest mycobacterial database

available, we annotate only up to 20% of the detectedmolecules

in any lipidome. The events corresponding to unnamed mole-

cules might derive from fragmentation or redundant detection

of unexpected adducts or might simply be molecules produced

byM. tuberculosis that are not known in the literature. Based on

the low rates of source fragmentation and redundant detection of

molecules in altered ionization states observed during the map-

ping process, it appears that knowledge of the mycobacterial


lipidome is far from complete. Indeed, the mycobacterial

genome has an unusually large number of lipid synthases, and

for many of these, their products remain unknown. These facts

are surprising given the worldwide scope of the tuberculosis

epidemic, with an estimated 1.6 million deaths per year (World

Health Organization, 2009). Subtraction of all entries comprising

MycoMass from those in any routinely generated lipidome

shown here provides a tangible list of unnamed molecules that

represents a map for solving the molecular toolkit of the world’s

most devastating bacterial pathogen.

SIGNIFICANCE

Mycobacterium tuberculosis remains one of the world’s

most deadly bacterial pathogens and survives within human

cells using a protective lipid envelope comprised of distinct

layers. This lipidic cell wall regulates uptake of nutrients and

antitubercular drugs while shedding lipid adjuvants, anti-

gens, and pathogen-specific markers of infection. To profile

mycobacteria on an organism-wide basis, we first solved a

static M. tuberculosis lipidomic dataset comprised of mass

spectrometry datasets, a lipid database containing more

than 5,000 neutral masses from medically relevant myco-

bacteria, and an accurate mass-retention time map of

more than 300 lipidswith 30 finemaps of alkyl chain variants.

Among 58 lipid types in the MycoMass and MycoMap data-

bases, more than 40 are lacking in eukaryotic or Gram-nega-

tive organisms, illustrating the need to move beyond model

organisms for direct study of the specialized molecules in

pathogens. We implemented a broadly separating, single-

step chromatography system together with automated ion

finding and statistical and annotation software to create a

platform for comparative lipidomics. This platform itera-

tively solves replicate lipidomes before and after infection

or among various clinical isolates to provide broadmeasure-

ments of pathogen response and chemotaxonomic infor-

mation. Pairwise comparison of �6,000 aligned features

describes chemical relatedness of mycobacteria with low

false-positive molecular discovery rates. Broad chemotaxo-

nomic analyses of mycobacteria measured the extent of

chemical change associated with species and strain-

specific variants and provided detailed lists of themolecules

changed. Unbiased scanning of a W Beijing strain of

M. tuberculosis identified the known biomarker phenolic

glycolipid and provided the basis for a new clinically appli-

cable test for forms of this glycolipid that have or have not

been associated with virulence.

EXPERIMENTAL PROCEDURES

MycoMass Database

Lipids for M. tuberculosis, M. smegmatis, M. bovis BCG, M. avium, M. leprae,

and M. marinum were reported according to LIPID MAPS conventions. Alkyl-

forms are variations in length and unsaturation of lipids based on all possible

lipid substitutions, except for PIMs and trehalose dimycolates in which

a smaller number of specific combinations are known to occur (Fujita et al.,

2005; Gilleron et al., 2006). From calculated neutral mass values (M), the

expected [M+H]+, [M+NH4]+, [M+Na]+, [M–H]�, [M+HCOO]�, and

[M+CH3COO]� are shown to five significant figures. Due to their distinctive

appearance in MS, [M+Fe54/56–2H]+ mycobactins and phosphatidyl ethanolo-

mine [2M+H]+ were listed for a total of 32,438 entries to the MycoMass


Chemistry & Biology


database (Figure S1) (http://www.brighamandwomens.org/research/depts/

medicine/rheumatology/labs/moody).

Mycobacterial Culture

M. smegmatis mc2155, M. bovis BCG, and M. tuberculosis H37Rv (Trudeau

Institute) and M. tuberculosis HN878 (Robert N. Husson) were cultured in

6 ml Middlebrook 7H9 broth supplemented with 10% oleic acid/albumin/

dextrose/catalase (Becton Dickinson) in 50 ml polystyrene tubes (Corning)

and shaken at 100 rpm at 37�C until visible growth appeared, up to 2 weeks

depending on the species. OnemL of the starter culture was transferred in trip-

licate to 45 ml fresh media in 250 ml sterile polystyrene containers with vented

caps and in singlicate to 45 ml of fresh media supplemented by 0.05% Tween

80 for growth monitoring by OD600 measurement. Triplicate Tween-free

cultures were harvested when the Tween culture reached 0.6 (±0.1) OD600.

W Beijing family clinical isolates were obtained from retreatment (subjects

#126 and #138) and one newly diagnosed case (subject #57) was obtained

from a TB natural history study (ClinicalTrials.gov identifier: NCT00341601)

at the National Masan Tuberculosis Hospital in the Republic of Korea. Cultures

were obtained following sputum processing, microscopic examination for

acid-fast bacilli and the BacT liquid culture system (Biomerieux) or from

Ogawa slants (ShinYang Chemicals, Korea) incubated at 37�C in ambient air

for a maximum of 8 weeks. Primary cultures were identified using classical

methods (Levy-Frebault and Portaels, 1992) and stored at �80�C. Drug

susceptibility testing was performed by the proportion method on Lowenstein-

Jensen medium using previously described methods (Canetti et al., 1969;

Wayne, 1974). For mass spectrometry, isolates were cultured in 20 ml of 7H9

medium supplemented with 10% albumin/dextrose/catalase (EMD Chemicals,

San Diego, CA) containing glycine-alanine salts and incubated in 250ml bottles

in a rolling incubator at 37�C.

Lipid Extraction

LC-MS grade solvents (Fisher) and clean borosilicate glassware (Fisher),

amber vials (Supelco), and Teflon-lined caps (Fisher) were used. Laboratory

strains were centrifuged (4,000 rpm,10 min) to clarify culture supernatants,

which were passed twice through a 0.22 mm filter to detect secreted

compounds. Cell pellets were washed twice in 10 ml Optima water, resus-

pended in 1 ml of CH3OH, transferred to a 50 ml amber glass bottle, and con-

tacted with 25 ml CHCl3/CH3OH (2:1, v/v) overnight to sterilize bacteria.

CHCl3/CH3OH suspensions were transferred in 50 ml conical glass tubes

and shaken on an Orbitron rotator for at least 1 hr. After centrifugation, lipid

extracts were decanted, and bacteria pellets were subjected to two additional

extractions using CHCl3:CH3OH (1:1, v/v) and CHCl3:CH3OH (1:2, v/v) with

pooling of extracts and evaporation with GeneVac EZ-2 (SP Scientific) using

the low boiling point mixture setting. Dried lipids were resuspended in

a minimum volume of CHCl3:CH3OH (1:1, v/v) and dried under nitrogen in pre-

weighed vials, then reweighed in triplicate on microbalance (Mettler Toledo,

XP205), and values were reported when fully dried as shown by replicate

measurements showing less than 1% variance. Using 2 mg of lipid ex-

tract, replicate measures showed variance of 20 mg, providing mass errors

below 1% for in vitro-derived samples. Extracts were then redissolved in

CHCl3:CH3OH (1:1, v/v) at 1 mg/mL. For clinical isolates, mid-log phase

cultures (OD = 0.5±1) were centrifuged at 3,000 rpm for 15 min and extracted

as described (Reed et al., 2004).

HPLC-Electrospray Ionization Quadrupole Time-of-Flight Mass

Spectrometry

Using an Agilent Technologies 6520 Accurate-Mass Q-Tof and a 1200 series

HPLC system with a Varian Monochrom diol column (3 mm 3 150 mm 3

2 mm) and a Varian Monochrom diol guard column (3 mm 3 4.6 mm), up to

50 mg of lipid extract was dried under nitrogen and resuspended at

0.5 mg/mL in solvent A (hexanes:isopropanol, 70:30 [v: v], 0.02% [m/v] formic

acid, 0.01% [m/v] ammonium hydroxide), filtered or centrifuged at 1,500 rpm

for 5 min to remove trace nonlipidic materials prior to transfer to a glass

autosampler vial (Agilent). Ten mg were injected, and the column (20 C) was

eluted at 0.15 ml/min with a binary gradient from 0% to 100% solvent B

(isopropanol:methanol, 70:30 [v/v], 0.02% [m/v] formic acid, 0.01% [m/v]

ammonium hydroxide): 0–10 min, 0% B; 17–22 min, 50% B; 30–35 min,

100% B; 40–44 min, 0% B, followed by additional 6 min 0% B postrun.


Ionization was maintained at 325�C with a 5 l/min drying gas flow, a 30 psig

nebulizer pressure, and 5,500 V. Spectra were collected in positive and nega-

tive ion mode from m/z 100 to 3,000 at 1 spectrum/s. Continuous infusion

calibrants included m/z 121.050873 and 922.009798 in positive ion mode

and m/z 112.985587 and 1033.98810 in negative ion mode. CID-MS was per-

formed with an energy of 30–60 V. For any large-scale comparative analysis,

the column is conditioned by three succesive 10 min elutions with B, B/A 1:1

(v/v) and A solvents followed by three mock injections with solvent cycling

before mycobacterial samples are analyzed.

HPLC-MS Data Extraction and Alignment

Raw data files were converted to mzData using MassHunter and processed in

R using the XCMS (version 1.24) (Smith et al., 2006) centWave peak finder

method designed for high mass accuracy data (Tautenhahn et al., 2008).

XCMS was downloaded from http://metlin.scripps.edu/xcms/index.php.

Briefly, peaks were deconvoluted and aligned across samples using an s/n

threshold of 5, a maximum tolerated m/z deviation of 10 ppm, a frame width

ofmzdiff = 0.001, a peak width of 20–120 s, and a band width of 5. The aligned

output consisted of accurate mass, retention time (RT = 1–2,640 s), and inten-

sity of each peak and was exported as .csv files for analysis in Mass Profiler

Professional or for automatic annotation in R. Although mass accuracy of

<2 ppm was achieved with optimized conditions, trace compounds from bio-

logical sources can provide lower mass accuracy, leading to a 10 ppm mass

window, which was validated to efficiently capture data points that describe

a chromatographic peak when detected in complex mixtures with varying

peak intensity.

Semi-automated Annotation

Features, medianm/z, andmedian RT information from XCMS output were ex-

ported and compared to the MycoMass database formatted as .csv files and

performed in R (version 2.11.1) using an in-house designed script. Initial

matches for a reference database yielded 1,020 and 768matches for the posi-

tive and negative ionmode, respectively, which were subsequently vetted with

AMRT data (MycoMap), reducing the number of annotations to 624 and 366,

respectively (Figure S4).

MycoMap Database

The dataset exported as an Excel file was displayed as retention time versus

m/z on an Excel scatter plot to identify clusters. One lead compound in each

group was assigned a chemical formula when it passed all tests: matching

the mass of a known lipid family in MycoMass within 10 ppm, matching the

alkylform patterns in MycoMass, matching the RT of standards when avail-

able, and showing expected fragmentation patterns (Figure S4). Retention

times were matched for triglycerides (trioleyl, Avanti), phthiocerol dimycocer-

osate and mannosyl phosphomycoketide (synthetic, A. J. Minnaard) (Casas-

Arce et al., 2008; van Summeren et al., 2006), sulfoglycolipids (purified,

M. Gilleron), phosphatidylinositol and monoacyl PIM2 (in-house purified),

mycobactin and carboxymycobactin (from M. bovis BCG, C. Ratledge), and

glucose monomycolate and trehalose monomycolate (Moody et al., 2002;

Moody et al., 2000). Finally, fine mapping assigned a molecular formula, m/z

and retention time to each alkylform to create MycoMap.

Comparative Lipidomics

XCMS data matrices listing detected features, median m/z, and median RT

of triplicate lipidic extracts were imported into Mass Profiler Professional

(B.02.00) for pairwise comparison (two strains in triplicate) using the Student’s

paired t test with multiple testing correction (Benjamini and Hochberg,

1995). Features were identified by database matching or collisional mass

spectrometry.

Mouse Infection

After aerosol inoculation of C3H mice with M. tuberculosis Erdman 2.5

(200 organisms/mouse), mice were sacrificed after 8 weeks. Lung pairs

were homogenized in PBS with a bead-beater as previously described

(Kamath and Behar, 2005). Homogenates were washed twice with 5 ml of

PBS (2,000 rpm, 10 min). The pelleted material was extracted in 3 ml of meth-

anol, vortexed, and transferred to 6 ml of chloroform and methanol and


http://www.brighamandwomens.org/research/depts/medicine/rheumatology/labs/moody

http://www.brighamandwomens.org/research/depts/medicine/rheumatology/labs/moody

http://ClinicalTrials.gov

http://metlin.scripps.edu/xcms/index.php

Chemistry & Biology


extracted as above. M. tuberculosis Erdman culture used for mouse infection

was maintained in parallel in triplicate and extracted as described above.

SUPPLEMENTAL INFORMATION

Supplemental Information includes seven figures and Supplemental

Experimental Procedures and can be found with this article online at

doi:10.1016/j.chembiol.2011.10.013.

ACKNOWLEDGMENTS

This work is supported by National Institutes of Health Grants U19 AI076217,

R01 AI071155, and AR048632; the Broad Institute; and the Burroughs

Wellcome Fund for Translational Research. The authors thank M. Gilleron

and C. Ratledge for providing lipid standards, R.N. Husson for providing

M. tuberculosis Beijing strains, and S. Fortune for reading the manuscript.

Received: July 25, 2011

Revised: September 18, 2011

Accepted: October 3, 2011

Published: December 22, 2011

REFERENCES

Adams, K.N., Takaki, K., Connolly, L.E., Wiedenhoft, H., Winglee, K., Humbert,

O., Edelstein, P.H., Cosma, C.L., and Ramakrishnan, L. (2011). Drug tolerance

in replicating mycobacteria mediated by a macrophage-induced efflux mech-

anism. Cell 145, 39–53.

Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate:

a practical and powerful approach to multiple testing. J. R. Stat. Soc. B 57,

289–300.

Borgstrom, B. (1952). Investigation on lipid separation methods. Separation of

phospholipids from neutral fat and fatty acids. Acta Physiol. Scand. 25,

101–110.

Canetti, G., Fox, W., Khomenko, A., Mahler, H.T., Menon, N.K., Mitchison,

D.A., Rist, N., and Smelev, N.A. (1969). Advances in techniques of testing

mycobacterial drug sensitivity, and the use of sensitivity tests in tuberculosis

control programmes. Bull. World Health Organ. 41, 21–43.

Casas-Arce, E., ter Horst, B., Feringa, B.L., and Minnaard, A.J. (2008).

Asymmetric total synthesis of PDIM A: a virulence factor of Mycobacterium

tuberculosis. Chemistry 14, 4157–4159.

Comas, I., Chakravartti, J., Small, P.M., Galagan, J., Niemann, S., Kremer, K.,

Ernst, J.D., and Gagneux, S. (2010). Human T cell epitopes of Mycobacterium

tuberculosis are evolutionarily hyperconserved. Nat. Genet. 42, 498–503.

Constant, P., Perez, E., Malaga, W., Laneelle, M.A., Saurel, O., Daffe, M., and

Guilhot, C. (2002). Role of the pks15/1 gene in the biosynthesis of phenolgly-

colipids in the Mycobacterium tuberculosis complex. Evidence that all strains

synthesize glycosylated p-hydroxybenzoic methyl esters and that strains

devoid of phenolglycolipids harbor a frameshift mutation in the pks15/1

gene. J. Biol. Chem. 277, 38148–38158.

Daffe, M., and Laneelle, M.A. (1988). Distribution of phthiocerol diester,

phenolic mycosides and related compounds in mycobacteria. J. Gen.

Microbiol. 134, 2049–2055.

Daffe, M., and Draper, P. (1998). The envelope layers of mycobacteria with

reference to their pathogenicity. Adv. Microb. Physiol. 39, 131–203.

de Carvalho, L.P., Fischer, S.M., Marrero, J., Nathan, C., Ehrt, S., and Rhee,

K.Y. (2010). Metabolomics of Mycobacterium tuberculosis reveals compart-

mentalized co-catabolism of carbon substrates. Chem. Biol. 17, 1122–1131.

Dennis, E.A., Deems, R.A., Harkewicz, R., Quehenberger, O., Brown, H.A.,

Milne, S.B., Myers, D.S., Glass, C.K., Hardiman, G., Reichart, D., et al.

(2010). A mouse macrophage lipidome. J. Biol. Chem. 285, 39976–39985.

Dormans, J., Burger, M., Aguilar, D., Hernandez-Pando, R., Kremer, K., Roholl,

P., Arend, S.M., and van Soolingen, D. (2004). Correlation of virulence, lung

pathology, bacterial load and delayed type hypersensitivity responses after


infection with different Mycobacterium tuberculosis genotypes in a BALB/c

mouse model. Clin. Exp. Immunol. 137, 460–468.

Fahy, E., Subramaniam, S., Brown, H.A., Glass, C.K., Merrill, A.H., Jr., Murphy,

R.C., Raetz, C.R., Russell, D.W., Seyama, Y., Shaw, W., et al. (2005). A

comprehensive classification system for lipids. J. Lipid Res. 46, 839–861.

Fruh, K., Finlay, B., and McFadden, G. (2010). On the road to systems biology

of host-pathogen interactions. Future Microbiol. 5, 131–133.

Fujita, Y., Naka, T., McNeil, M.R., and Yano, I. (2005). Intact molecular charac-

terization of cord factor (trehalose 6,60-dimycolate) from nine species of myco-

bacteria by MALDI-TOF mass spectrometry. Microbiology 151, 3403–3416.

Geisel, R.E., Sakamoto, K., Russell, D.G., and Rhoades, E.R. (2005). In vivo

activity of released cell wall lipids of Mycobacterium bovis bacillus

Calmette-Guerin is due principally to trehalose mycolates. J. Immunol. 174,

5007–5015.

Gilleron, M., Lindner, B., and Puzo, G. (2006). MS/MS approach for character-

ization of the fatty acid distribution on mycobacterial phosphatidyl-myo-

inositol mannosides. Anal. Chem. 78, 8543–8548.

Glynn, J.R., Whiteley, J., Bifani, P.J., Kremer, K., and van Soolingen, D. (2002).

Worldwide occurrence of Beijing/W strains of Mycobacterium tuberculosis:

a systematic review. Emerg. Infect. Dis. 8, 843–849.

Hoffmann, C., Leis, A., Niederweis, M., Plitzko, J.M., and Engelhardt, H. (2008).

Disclosure of the mycobacterial outer membrane: cryo-electron tomography

and vitreous sections reveal the lipid bilayer structure. Proc. Natl. Acad. Sci.

USA 105, 3963–3967.

Homolka, S., Niemann, S., Russell, D.G., and Rohde, K.H. (2010). Functional

genetic diversity among Mycobacterium tuberculosis complex clinical

isolates: delineation of conserved core and lineage-specific transcriptomes

during intracellular survival. PLoS Pathog. 6, e1000988.

Jain, M., Petzold, C.J., Schelle, M.W., Leavell, M.D., Mougous, J.D., Bertozzi,

C.R., Leary, J.A., and Cox, J.S. (2007). Lipidomics reveals control of

Mycobacterium tuberculosis virulence lipids via metabolic coupling. Proc.

Natl. Acad. Sci. USA 104, 5133–5138.

Kamath, A.B., and Behar, S.M. (2005). Anamnestic responses of mice

following Mycobacterium tuberculosis infection. Infect. Immun. 73, 6110–

6118.

Kondo, E., Kanai, K., Nishimura, K., and Tsumita, T. (1970). Analysis of host-

originated lipids associated with ‘‘in vivo grown tubercle bacilli’’. Jpn. J.

Med. Sci. Biol. 23, 315–326.

Kruh, N.A., Troudt, J., Izzo, A., Prenni, J., and Dobos, K.M. (2010). Portrait of

a pathogen: the Mycobacterium tuberculosis proteome in vivo. PLoS ONE 5,

e13938.

Kruve, A., Herodes, K., and Leito, I. (2010). Electrospray ionization matrix

effect as an uncertainty source in HPLC/ESI-MS pesticide residue analysis.

J. AOAC Int. 93, 306–314.

Lakshmanan, V., Rhee, K.Y., and Daily, J.P. (2011). Metabolomics and malaria

biology. Mol. Biochem. Parasitol. 175, 104–111.

Levy-Frebault, V.V., and Portaels, F. (1992). Proposed minimal standards for

the genus Mycobacterium and for description of new slowly growing

Mycobacterium species. Int. J. Syst. Bacteriol. 42, 315–323.

Mahrous, E.A., Lee, R.B., and Lee, R.E. (2008). A rapid approach to lipid

profiling of mycobacteria using 2D HSQC NMR maps. J. Lipid Res. 49,

455–463.

Malaga, W., Constant, P., Euphrasie, D., Cataldi, A., Daffe, M., Reyrat, J.M.,

and Guilhot, C. (2008). Deciphering the genetic bases of the structural diversity

of phenolic glycolipids in strains of the Mycobacterium tuberculosis complex.

J. Biol. Chem. 283, 15177–15184.

Manca, C., Tsenova, L., Freeman, S., Barczak, A.K., Tovey, M., Murray, P.J.,

Barry, C., and Kaplan, G. (2005). Hypervirulent M. tuberculosis W/Beijing

strains upregulate type I IFNs and increase expression of negative regulators

of the Jak-Stat pathway. J. Interferon Cytokine Res. 25, 694–701.

Marrero, J., Rhee, K.Y., Schnappinger, D., Pethe, K., and Ehrt, S. (2010).

Gluconeogenic carbon flow of tricarboxylic acid cycle intermediates is critical

for Mycobacterium tuberculosis to establish andmaintain infection. Proc. Natl.

Acad. Sci. USA 107, 9819–9824.


http://dx.doi.org/doi:10.1016/j.chembiol.2011.10.013

Chemistry & Biology


Matsunaga, I., Bhatt, A., Young, D.C., Cheng, T.Y., Eyles, S.J., Besra, G.S.,

Briken, V., Porcelli, S.A., Costello, C.E., Jacobs, W.R., Jr., and Moody, D.B.

(2004). Mycobacterium tuberculosis pks12 produces a novel polyketide pre-

sented by CD1c to T cells. J. Exp. Med. 200, 1559–1569.

Moody, D.B., Guy, M.R., Grant, E., Cheng, T.Y., Brenner, M.B., Besra, G.S.,

and Porcelli, S.A. (2000). CD1b-mediated T cell recognition of a glycolipid

antigen generated from mycobacterial lipid and host carbohydrate during

infection. J. Exp. Med. 192, 965–976.

Moody, D.B., Briken, V., Cheng, T.Y., Roura-Mir, C., Guy, M.R., Geho, D.H.,

Tykocinski, M.L., Besra, G.S., and Porcelli, S.A. (2002). Lipid length controls

antigen entry into endosomal and nonendosomal pathways for CD1b presen-

tation. Nat. Immunol. 3, 435–442.

Perez, E., Constant, P., Laval, F., Lemassu, A., Laneelle, M.A., Daffe, M., and

Guilhot, C. (2004). Molecular dissection of the role of twomethyltransferases in

the biosynthesis of phenolglycolipids and phthiocerol dimycoserosate in the

Mycobacterium tuberculosis complex. J. Biol. Chem. 279, 42584–42592.

Quehenberger, O., Armando, A.M., Brown, A.H., Milne, S.B., Myers, D.S.,

Merrill, A.H., Bandyopadhyay, S., Jones, K.N., Kelly, S., Shaner, R.L., et al.

(2010). Lipidomics reveals a remarkable diversity of lipids in human plasma.

J. Lipid Res. 51, 3299–3305.

Raman, S., Puyang, X., Cheng, T.Y., Young, D.C., Moody, D.B., and Husson,

R.N. (2006). Mycobacterium tuberculosis SigM positively regulates Esx

secreted protein and nonribosomal peptide synthetase genes and down regu-

lates virulence-associated surface lipid synthesis. J. Bacteriol. 188, 8460–

8468.

Reed, M.B., Domenech, P., Manca, C., Su, H., Barczak, A.K., Kreiswirth, B.N.,

Kaplan, G., and Barry, C.E., 3rd. (2004). A glycolipid of hypervirulent tubercu-

losis strains that inhibits the innate immune response. Nature 431, 84–87.

Rohde, K.H., Abramovitch, R.B., and Russell, D.G. (2007). Mycobacterium

tuberculosis invasion of macrophages: linking bacterial gene expression to

environmental cues. Cell Host Microbe 2, 352–364.

Rustad, T.R., Harrell, M.I., Liao, R., and Sherman, D.R. (2008). The enduring

hypoxic response of Mycobacterium tuberculosis. PLoS One 3, e1502.

Sartain, M.J., Dick, D.L., Rithner, C.D., Crick, D.C., and Belisle, J.T. (2011).

Lipidomic analyses of Mycobacterium tuberculosis based on accurate mass

measurements and the novel ‘‘Mtb LipidDB’’. J. Lipid. Res. 52, 861–872.

Schnappinger, D., Ehrt, S., Voskuil, M.I., Liu, Y., Mangan, J.A., Monahan, I.M.,

Dolganov, G., Efron, B., Butcher, P.D., Nathan, C., and Schoolnik, G.K. (2003).


Transcriptional Adaptation of Mycobacterium tuberculosis within

Macrophages: Insights into the Phagosomal Environment. J. Exp. Med. 198,

693–704.

Singh, A., Crossman, D.K., Mai, D., Guidry, L., Voskuil, M.I., Renfrow, M.B.,

and Steyn, A.J. (2009). Mycobacterium tuberculosis WhiB3 maintains redox

homeostasis by regulating virulence lipid anabolism to modulate macrophage

response. PLoS Pathog. 5, e1000545.

Smith, C.A., Want, E.J., O’Maille, G., Abagyan, R., and Siuzdak, G. (2006).

XCMS: processing mass spectrometry data for metabolite profiling using

nonlinear peak alignment, matching, and identification. Anal. Chem. 78,

779–787.

Song, S.H., Park, K.U., Lee, J.H., Kim, E.C., Kim, J.Q., and Song, J. (2009).

Electrospray ionization-tandem mass spectrometry analysis of the mycolic

acid profiles for the identification of common clinical isolates of mycobacterial

species. J. Microbiol. Methods 77, 165–177.

Takayama, K., and Armstrong, E.L. (1976). Isolation, characterization, and

function of 6-mycolyl-60-acetyltrehalose in the H37Ra strain of

Myocobacterium tuberculosis. Biochemistry 15, 441–447.

Tautenhahn, R., Bottcher, C., and Neumann, S. (2008). Highly sensitive feature

detection for high resolution LC/MS. BMC Bioinformatics 9, 504.

Taylor, P.J. (2005). Matrix effects: the Achilles heel of quantitative high-perfor-

mance liquid chromatography-electrospray-tandemmass spectrometry. Clin.

Biochem. 38, 328–334.

van Meer, G., Leeflang, B.R., Liebisch, G., Schmitz, G., and Goni, F.M. (2007).

The European lipidomics initiative: enabling technologies. Methods Enzymol.

432, 213–232.

van Summeren, R.P., Moody, D.B., Feringa, B.L., and Minnaard, A.J. (2006).

Total synthesis of enantiopure beta-D-mannosyl phosphomycoketides from

Mycobacterium tuberculosis. J. Am. Chem. Soc. 128, 4546–4547.

Wayne, L.G. (1974). Simple pyrazinamidase and urease tests for routine iden-

tification of mycobacteria. Am. Rev. Respir. Dis. 109, 147–151.

World Health Organization. (2009). Global Tuberculosis Control 2009:

Epidemiology Strategy Financing (Geneva, Switzerland: World Health

Organization).

Zuber, B., Chami, M., Houssin, C., Dubochet, J., Griffiths, G., and Daffe, M.

(2008). Direct visualization of the outer membrane of mycobacteria and

corynebacteria in their native state. J. Bacteriol. 190, 5672–5680.


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A Comparative Lipidomics Platform for Chemotaxonomic …€¦ · and ionization protocols. Chromatographic Design We harvested mycobacteria from plates, broth, or infected mice and